Implements Example 33: Oscillating Search in very high-dimensional feature selection., see also Example 33 source code
/* ========================================================================= Feature Selection Toolbox 3 source code --------------------------------------- */ /* ========================================================================= Copyright: * FST3 software (with exception of any externally linked libraries) is copyrighted by Institute of Information Theory and Automation (UTIA), Academy of Sciences of the Czech Republic. * FST3 source codes as presented here do not contain code of third parties. FST3 may need linkage to external libraries to exploit its functionality in full. For details on obtaining and possible usage restrictions of external libraries follow their original sources (referenced from FST3 documentation wherever applicable). * FST3 software is available free of charge for non-commercial use. Please address all inquires concerning possible commercial use of FST3, or if in doubt, to FST3 maintainer (see http://fst.utia.cz) * Derivative works based on FST3 are permitted as long as they remain non-commercial only. * Re-distribution of FST3 software is not allowed without explicit consent of the copyright holder. Disclaimer of Warranty: * FST3 software is presented "as is", without warranty of any kind, either expressed or implied, including, but not limited to, the implied warranties of merchantability and fitness for a particular purpose. The entire risk as to the quality and performance of the program is with you. Should the program prove defective, you assume the cost of all necessary servicing, repair or correction. Limitation of Liability: * The copyright holder will in no event be liable to you for damages, including any general, special, incidental or consequential damages arising out of the use or inability to use the code (including but not limited to loss of data or data being rendered inaccurate or losses sustained by you or third parties or a failure of the program to operate with any other programs). ========================================================================== */ #include <boost/smart_ptr.hpp> #include <exception> #include <iostream> #include <cstdlib> #include <string> #include <vector> #include "error.hpp" #include "global.hpp" #include "subset.hpp" #include "data_intervaller.hpp" #include "data_splitter.hpp" //#include "data_splitter_5050.hpp" //#include "data_splitter_cv.hpp" //#include "data_splitter_holdout.hpp" //#include "data_splitter_leave1out.hpp" //#include "data_splitter_resub.hpp" #include "data_splitter_randrand.hpp" //#include "data_splitter_randfix.hpp" #include "data_scaler.hpp" #include "data_scaler_void.hpp" //#include "data_scaler_to01.hpp" //#include "data_scaler_white.hpp" #include "data_accessor_splitting_memTRN.hpp" #include "data_accessor_splitting_memARFF.hpp" //#include "criterion_normal_bhattacharyya.hpp" //#include "criterion_normal_gmahalanobis.hpp" //#include "criterion_normal_divergence.hpp" #include "criterion_multinom_bhattacharyya.hpp" #include "criterion_wrapper.hpp" //#include "criterion_wrapper_bias_estimate.hpp" //#include "criterion_subsetsize.hpp" //#include "criterion_sumofweights.hpp" //#include "criterion_negative.hpp" //#include "distance_euclid.hpp" //#include "distance_L1.hpp" //#include "distance_Lp.hpp" //#include "classifier_knn.hpp" //#include "classifier_normal_bayes.hpp" #include "classifier_multinom_naivebayes.hpp" //#include "classifier_svm.hpp" #include "search_bif.hpp" //#include "search_bif_threaded.hpp" //#include "search_monte_carlo.hpp" //#include "search_monte_carlo_threaded.hpp" //#include "search_exhaustive.hpp" //#include "search_exhaustive_threaded.hpp" //#include "branch_and_bound_predictor_averaging.hpp" //#include "search_branch_and_bound_basic.hpp" //#include "search_branch_and_bound_improved.hpp" //#include "search_branch_and_bound_partial_prediction.hpp" //#include "search_branch_and_bound_fast.hpp" #include "seq_step_straight.hpp" //#include "seq_step_straight_threaded.hpp" //#include "seq_step_hybrid.hpp" //#include "seq_step_ensemble.hpp" //#include "search_seq_sfs.hpp" //#include "search_seq_sffs.hpp" //#include "search_seq_sfrs.hpp" #include "search_seq_os.hpp" //#include "search_seq_dos.hpp" //#include "result_tracker_dupless.hpp" //#include "result_tracker_regularizer.hpp" //#include "result_tracker_feature_stats.hpp" //#include "result_tracker_stabileval.hpp" int main() { try{ typedef double RETURNTYPE; typedef double DATATYPE; typedef double REALTYPE; typedef unsigned int IDXTYPE; typedef unsigned int DIMTYPE; typedef short BINTYPE; typedef FST::Subset<BINTYPE, DIMTYPE> SUBSET; typedef FST::Data_Intervaller<std::vector<FST::Data_Interval<IDXTYPE> >,IDXTYPE> INTERVALLER; typedef boost::shared_ptr<FST::Data_Splitter<INTERVALLER,IDXTYPE> > PSPLITTER; typedef FST::Data_Splitter_RandomRandom<INTERVALLER,IDXTYPE,BINTYPE> SPLITTERRR; //typedef FST::Data_Accessor_Splitting_MemTRN<DATATYPE,IDXTYPE,INTERVALLER> DATAACCESSOR; // uncomment for TRN data format typedef FST::Data_Accessor_Splitting_MemARFF<DATATYPE,IDXTYPE,INTERVALLER> DATAACCESSOR; // uncomment for ARFF data format typedef FST::Criterion_Multinomial_Bhattacharyya<RETURNTYPE,DATATYPE,REALTYPE,IDXTYPE,DIMTYPE,SUBSET,DATAACCESSOR> BHATTMULTINOMIALDIST; typedef FST::Classifier_Multinomial_NaiveBayes<RETURNTYPE,DATATYPE,REALTYPE,IDXTYPE,DIMTYPE,SUBSET,DATAACCESSOR> CLASSIFIERMULTINOMIAL; typedef FST::Sequential_Step_Straight<RETURNTYPE,DIMTYPE,SUBSET,BHATTMULTINOMIALDIST> EVALUATOR; std::cout << "Starting Example 33: Oscillating Search in very high-dimensional feature selection..." << std::endl; // randomly sample 50% of data for training and randomly sample (disjunct) 40% for independent testing of final classification performance PSPLITTER dsp_outer(new SPLITTERRR(1, 50, 40)); // (there will be one outer randomized split only) // do not scale data boost::shared_ptr<FST::Data_Scaler<DATATYPE> > dsc(new FST::Data_Scaler_void<DATATYPE>()); // set-up data access boost::shared_ptr<std::vector<PSPLITTER> > splitters(new std::vector<PSPLITTER>); splitters->push_back(dsp_outer); boost::shared_ptr<DATAACCESSOR> da(new DATAACCESSOR("data/reuters_apte.arff",splitters,dsc)); da->initialize(); // initiate access to split data parts da->setSplittingDepth(0); if(!da->getFirstSplit()) throw FST::fst_error("50/40 random data split failed."); // initiate the storage for subset to-be-selected boost::shared_ptr<SUBSET> sub(new SUBSET(da->getNoOfFeatures())); // set-up multinomial Bhattacharyya distance criterion boost::shared_ptr<BHATTMULTINOMIALDIST> dmultinom(new BHATTMULTINOMIALDIST); dmultinom->initialize(da); // (initialization = multinomial model parameter estimation on training data) // set-up individual feature ranking to serve as OS initialization FST::Search_BIF<RETURNTYPE,DIMTYPE,SUBSET,BHATTMULTINOMIALDIST> srch_bif; // set-up the standard sequential search step object (option: hybrid, ensemble, etc.) boost::shared_ptr<EVALUATOR> eval(new EVALUATOR); // set-up the Oscillating Search procedure in its fastest setting FST::Search_OS<RETURNTYPE,DIMTYPE,SUBSET,BHATTMULTINOMIALDIST,EVALUATOR> srch(eval); srch.set_delta(1); // target subset size must be set because a) Bhattacharyya is monotonous with respect to subset size, // b) in very-high-dimensional problem d-optimizing search is not feasible due to search complexity DIMTYPE target_subsize=500; // run the search - first find the initial subset by means of BIF, then improve it by means of OS std::cout << "Feature selection setup:" << std::endl << *da << std::endl << srch_bif << std::endl << srch << std::endl << *dmultinom << std::endl << std::endl; RETURNTYPE critval_train, critval_test; if(!srch_bif.search(target_subsize,critval_train,sub,dmultinom,std::cout)) throw FST::fst_error("Search (BIF) not finished."); std::cout << std::endl << "Initialization result: " << std::endl << *sub << "Criterion value=" << critval_train << std::endl << std::endl; if(!srch.search(target_subsize,critval_train,sub,dmultinom,std::cout)) throw FST::fst_error("Search (OS) not finished."); std::cout << std::endl << "Search result: " << std::endl << *sub << "Criterion value=" << critval_train << std::endl; // (optionally) validate result by estimating Naive Multinomial Bayes classifier accuracy on selected feature sub-space on independent test data boost::shared_ptr<CLASSIFIERMULTINOMIAL> cmultinom(new CLASSIFIERMULTINOMIAL); cmultinom->initialize(da); cmultinom->train(da,sub); cmultinom->test(critval_test,da); std::cout << "Validated Multinomial NaiveBayes accuracy=" << critval_test << std::endl << std::endl; } catch(FST::fst_error &e) {std::cerr<<"FST ERROR: "<< e.what() << ", code=" << e.code() << std::endl;} catch(std::exception &e) {std::cerr<<"non-FST ERROR: "<< e.what() << std::endl;} return 0; }
1.6.1